CogSci'25Clicking, Fast and Slow: Towards Intuitive and Analytical Behaviors Modeling for Recommender Systems
Abstract
Recommender systems personalize content delivery based on users' interaction history. However, not all clicks result from deliberate decisions—many arise from intuitive reactions. Inspired by the dual process theory, we argue that intuitive clicks are primarily driven by System 1, reacting to superficial cues, while analytical clicks involve deeper processing by System 2, considering the semantic meaning and long-term preference. However, existing models overlook these cognitive mechanisms. To address this, we propose DualRec, a novel recommendation method that models both intuitive and analytical behaviors. DualRec encodes items using language models, leveraging shallow layers for superficial understanding (System 1) and deep layers for semantic comprehension (System 2). It employs Transformer-based encoders with two attention mechanisms to capture intuitive "fast" and analytical "slow" click patterns. A learnable fusion layer balances these behaviors. Extensive experiments demonstrate that DualRec outperforms existing methods and highlights the importance of integrating both cognitive processes in recommendations.
Poster
Cite our work!
@inproceedings{wu2025clicking,
title={Clicking, Fast and Slow: Towards Intuitive and Analytical Behaviors Modeling for Recommender Systems},
author={Wu, Youlin and Zhan, Haoxi and Sun, Yuanyuan and Zhu, Haohao and Xu, Bo and Yang, Liang and Lin, Hongfei},
booktitle={Proceedings of the Annual Meeting of the Cognitive Science Society},
volume={47},
year={2025}
}